Machine‐learning‐based approach for predicting postoperative skeletal changes for orthognathic surgical planning

Background Manually surgical planning becomes an increasing workload of surgeons because of the fast‐growing patient population. This study introduced a machine‐learning‐based approach to assist surgical planning in orthognathic surgery. Methods Both preoperative and one‐year‐later postoperative com...

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Bibliographic Details
Published in:The international journal of medical robotics + computer assisted surgery 2022-06, Vol.18 (3), p.e2379-n/a
Main Authors: Ma, Qingchuan, Kobayashi, Etsuko, Fan, Bowen, Hara, Kazuaki, Nakagawa, Keiichi, Masamune, Ken, Sakuma, Ichiro, Suenaga, Hideyuki
Format: Article
Language:English
Subjects:
3D cephalometry
Artificial neural networks
automatic landmarking
Computed tomography
Feasibility studies
Machine learning
orthognathic surgery
Postoperative period
Surgeons
surgical planning
Two dimensional models
Workload
Workloads
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Summary:Background Manually surgical planning becomes an increasing workload of surgeons because of the fast‐growing patient population. This study introduced a machine‐learning‐based approach to assist surgical planning in orthognathic surgery. Methods Both preoperative and one‐year‐later postoperative computerised tomography images of 56 patients were collected. A 12‐layers cascaded deep neural network structure with two successive models was proposed to yield an end‐to‐end solution, where the first model extracts landmarks from 2D patches of 3D volume and the second model predicts postoperative skeletal changes. Results The experimental results showed that the model obtained a prediction accuracy of 5.4 mm at the landmark level in 42.9 s. It also represented 74.4% of 3D regions at volume level when compared with the ground truth of human surgeons. Conclusions This study demonstrated the feasibility of predicting postoperative skeletal changes for orthognathic surgical planning by using machine learning, showing great potential for reducing the workload of surgeons.
ISSN:1478-5951
1478-596X
DOI:10.1002/rcs.2379